Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose
Abstract
:1. Introduction
2. Materials and Methods
2.1. Participants
2.2. Robotic System
2.3. Interventions
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- A warm-up phase involving games (phase 1);
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- A training phase based on exercises (phase 2);
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- A relaxation phase involving games (phase 3).
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- Assist-as-needed modality: The patient has to reach the target with assistance tailored into the performance;
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- Unassisted modality: The patient has to reach the target without any assistance;
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- Against resistance: The patient has to reach the target against a resistance.
2.4. Clinical Evaluation Procedure Pre- and Post-Intervention
2.5. Data Collected during Interventions
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- The number of movements performed by the patient in each training modality used (assist as needed, unassisted, or against resistance);
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- The actual practice time in each training modality used (assist as needed, unassisted, or against resistance (in min);
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- The distance to be covered to reach the target used (defined by the therapist before each exercise).
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- The mean number of repeated movements in each training modality used;
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- The mean actual practice time in each training modality used (in percentage; normalized to a session length of 60 min);
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- The mean total distance traveled in each training modality used (in cm).
2.6. Statistical Analysis
3. Results
3.1. Clinical Outcomes Pre- and Post-Intervention
3.2. Practice Time during Robot-Assisted Therapy Session
3.3. Outcomes with All Robotic Training Modalities Pooled
3.4. Outcomes for Each Robotic Training Modality
4. Discussion
4.1. Measurement of Dimensions of Dose Using a Robotic Device?
4.2. A New Paradigm for a Combined and Progressive Approach to the Use of the Training Modalities?
4.3. Optimizing Session Productivity?
4.4. What Are the Clinical Benefits of Combining RT with Conventional Upper Limb Therapy?
4.5. Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
FMA | Fugl–Meyer Assessment |
RT | Robot-assisted therapy |
ANOVA | Analysis of variance |
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Participants (n) | 36 |
Age (years) | 59(16) |
Female (n) | 15 |
Side of paresis (n) | R (19), L (17) |
Etiology (n) | I (25), H (11) |
Time since the stroke at program initiation (days) | 54 (26) |
Duration of the program (days) | 31 (11) |
Initial FMA score (/66 pts) | 23 (17) |
Fugl–Meyer | Pre-Intervention | Post-Intervention | Gain | p-Value |
---|---|---|---|---|
Overall (66 points) | 23.1 (16.8) | 34.3 (18.9) | 11.2 (9.6) | <0.001 |
Shoulder/elbow (36 points) | 14.6 (8.8) | 20.9 (9.4) | 6.3 (5.4) | <0.001 |
Wrist (10 points) | 2.5 (3.4) | 4.2 (3.8) | 1.7 (2.1) | <0.001 |
Hand (14 points) | 4.3 (4.5) | 6.9 (5.2) | 2.6 (3.3) | <0.001 |
Coordination velocity (6 points) | 1.8 (1.6) | 2.3 (1.8) | 0.5 (1.2) | 0.013 |
Mean Actual Practice Time (%) | Mean Number of Repeated Movements | Mean Total Traveled Distance (cm) | |
---|---|---|---|
Main effect p-value | ns | <0.001 | <0.001 |
Session 1 | 47 (16) | 624 (308) | 6766 (4510) |
Session 2 | 50 (14) | 674 (264) | 7294 (4437) |
Session 3 | 49 (15) | 676 (321) | 7476 (4975) |
Session 4 | 47 (14) | 694 (282) | 7422 (4436) |
Session 5 | 46 (12) | 725 (258) | 7887 (4211) |
Session 6 | 50 (13) | 754 (318) a | 8508 (4775) a |
Session 7 | 50 (14) | 772 (322) a | 9007 (5045) a |
Session 8 | 49 (13) | 771 (336) a | 8991 (5131) a |
Session 9 | 51 (16) | 797 (360) a,b,c | 9338 (5555) a,b,c,d |
Session 10 | 51 (15) | 777 (362) a | 9403 (5494) a,b,c,d |
Session 11 | 51 (15) | 805 (389) a | 9955 (5818) a,b,c,d,e |
Session 12 | 50 (14) | 754 (342) | 9096 (4941) a |
Session 13 | 52 (12) | 806 (391) a | 10104 (5677) a,b,c,d,e |
Session 14 | 50 (13) | 796 (364) a,b,c | 9999 (5123) a,b,c,d,e |
Session 15 | 46 (13) | 764 (373) a | 9593 (5141) a,b,c,d |
Session 1–Session 15 changes | −1 (20) | 140 (381) | 2828 (4943) |
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Jamin, P.; Duret, C.; Hutin, E.; Bayle, N.; Koeppel, T.; Gracies, J.-M.; Pila, O. Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose. Sensors 2022, 22, 2989. https://doi.org/10.3390/s22082989
Jamin P, Duret C, Hutin E, Bayle N, Koeppel T, Gracies J-M, Pila O. Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose. Sensors. 2022; 22(8):2989. https://doi.org/10.3390/s22082989
Chicago/Turabian StyleJamin, Pascal, Christophe Duret, Emilie Hutin, Nicolas Bayle, Typhaine Koeppel, Jean-Michel Gracies, and Ophélie Pila. 2022. "Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose" Sensors 22, no. 8: 2989. https://doi.org/10.3390/s22082989